wwm_vocab.txt"
# model_path = "./roberta_wwm_pytorch_model.bin"
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# target = ["O", "B-DRUG", "B-DRUG_INGREDIENT", "B-DISEASE", "B-SYMPTOM", "B-SYNDROME", "B-DISEASE_GROUP", 
#         "B-FOOD", "B-FOOD_GROUP", "B-PERSON_GROUP", "B-DRUG_GROUP", "B-DRUG_DOSAGE", "B-DRUG_TASTE",
#          "B-DRUG_EFFICACY", "I-DRUG", "I-DRUG_INGREDIENT", "I-DISEASE", "I-SYMPTOM", "I-SYNDROME", "I-DISEASE_GROUP", 
#         "I-FOOD", "I-FOOD_GROUP", "I-PERSON_GROUP", "I-DRUG_GROUP", "I-DRUG_DOSAGE", "I-DRUG_TASTE",
#          "I-DRUG_EFFICACY"]

# model = load_bert(vocab_path, model_name="roberta", model_class="sequence_labeling_crf", target_size=len(target), simplfied=True)
# model.to(device)
# load_recent_model(model, "./bert_ner_model_crf.bin", device=device)

# word2idx = load_chinese_base_vocab(vocab_path, simplfied=True